Event Detection with Multi-Order Graph Convolution and Aggregated Attention

Haoran Yan, Xiaolong Jin, Xiangbin Meng, Jiafeng Guo, Xueqi Cheng


Abstract
Syntactic relations are broadly used in many NLP tasks. For event detection, syntactic relation representations based on dependency tree can better capture the interrelations between candidate trigger words and related entities than sentence representations. But, existing studies only use first-order syntactic relations (i.e., the arcs) in dependency trees to identify trigger words. For this reason, this paper proposes a new method for event detection, which uses a dependency tree based graph convolution network with aggregative attention to explicitly model and aggregate multi-order syntactic representations in sentences. Experimental comparison with state-of-the-art baselines shows the superiority of the proposed method.
Anthology ID:
D19-1582
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
5766–5770
Language:
URL:
https://aclanthology.org/D19-1582
DOI:
10.18653/v1/D19-1582
Bibkey:
Cite (ACL):
Haoran Yan, Xiaolong Jin, Xiangbin Meng, Jiafeng Guo, and Xueqi Cheng. 2019. Event Detection with Multi-Order Graph Convolution and Aggregated Attention. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5766–5770, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Event Detection with Multi-Order Graph Convolution and Aggregated Attention (Yan et al., EMNLP 2019)
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PDF:
https://aclanthology.org/D19-1582.pdf